Snyk founder's new venture, TESOL, posits that AI will make code disposable. Instead of code being the source of truth, a durable, versioned 'spec' document defining requirements will become the core asset. AI agents will generate the implementation, fundamentally changing software development.

Verifying complex systems is bottlenecked by the human inability to specify all requirements. The future of software development is an interactive process where AI helps propose specifications (e.g., via test generation) and then uses a prover to formally verify them.

Current AI coding assistants still require engineers to verify correctness. The future involves moving from this 'vibe coding' to a system where developers specify requirements in natural language. An AI, likely an EBM, would then generate formally verified code that is guaranteed to be logically compatible with the existing codebase.

Writing formally verified code, which can be mathematically proven to be secure, has been a niche practice due to its extreme difficulty for humans. Because AI agents don't get bored or frustrated, they could be tasked with writing code in these secure languages, making high-assurance programming practical for the first time.

A major hurdle for formal methods is the effort required to write proofs. Generative AI is becoming capable of producing proofs in formal languages like Lean, which can then be automatically verified by a machine. This could make verified software development scalable for the first time.

Instead of struggling to write an abstract mathematical specification, developers can write a simple, inefficient, but correct version of their program. This 'naive' implementation can then be used as a formal spec for an AI to generate an optimized version, along with a proof of its equivalence.

Formal verification, the process of mathematically proving software correctness, has been too complex for widespread use. New AI models can now automate this, allowing developers to build systems with mathematical guarantees against certain bugs—a huge step for creating trust in high-stakes financial software.

The role of a senior developer is evolving. They now focus on defining outcomes by writing tests that a piece of code must accomplish. The AI then generates the actual implementation, allowing small teams to build complex systems in a fraction of the traditional time.

The next frontier for mathematical AI isn't just solving conjectures, but applying its logic to software engineering. MSI can be used for formal verification, creating code that is provably correct and immune to entire classes of security bugs. This will move cybersecurity from a cat-and-mouse game to a state of mathematical certainty.